# To prepare • Review the attached datasets. • Construct a research question with

To prepare

• Review the attached datasets.

• Construct a research question with social change implications based on one of those datasets.

• Be sure to focus on the assumptions of this test and ask yourself, “Does it make sense to interpret the mean of this dependent variable?”

• Remember that you will need categorical predictor variables.

The Assignment

Use SPSS to answer the research question you constructed. Write an analysis in APA format and addresses each of the tasks listed below. The content should be 3 pages, including setup of the assignment, results, and interpretation of results. Your SPSS output should be included as an appendix.

1. What is the null hypothesis for your question?

2. What research design(s) would align with this question?

3. What dependent variable was used and how is it measured?

4. What independent variable is used and how is it measured?

5. If you found significance, what is the strength of the effect?

6. What is the answer to your research question?

7. What are the possible implications of social change?

Provide syntax of your analysis

Provide output of your analysis

Provide Hypos (hint – you will have 3 sets of hypos)

Provide write up of results.

Provide graph of your 2 x 3 FA

Early in your Assignment, when you relate which dataset you analyzed, please include the mean of the following variables. If you are using the Afrobarometer Dataset, report the mean of Q1 (Age). If you are using the General Social Survey Dataset, report the mean of Age. If you are using the HS Long Survey Dataset, report the mean of X1SES. See pages 533 and 534 in your Warner textbook for an excellent APA-compliant write-up of a factorial analysis of variance.

Warner, R. M. (2013). Applied statistics: From bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: SAGE Publications.

Chapter 13, “Factorial Analysis of Variance” (pp. 501–546)

This chapter demonstrates the utility of adding a second factor to ANOVA, paving the way to much more sophisticated research questions and output.